Identification Of Dynamic Models Research Articles

Parameter identification of energy consumption dynamic model for double-motor-driven belt conveyers based on Actor–Critic framework

Parameter identification of energy consumption dynamic model for double-motor-driven belt conveyers based on Actor–Critic framework

Dynamic modeling and parameter identification for a gantry-type automated fiber placement machine

Dynamic models play a critical role in the design of model-based controllers, which has a significant effect on the dynamic characteristics of motion equipment. This paper mainly focuses on the dynamic modeling and parameter identification for a gantry-type automated fiber placement (AFP) machine. First, a dynamic modeling process combining prismatic axes and revolute axes is conducted by Newton-Euler method, in which the effects of friction and hydraulic balance system are also considered. Then, as the convenience for parameter identification and the application in linearity control, the methods of dynamic model linearization and determination of minimum inertial parameters based on the multi-body system (MBS) theory are proposed, with which a dynamic model in the form of linearized minimum inertial parameters is consequently established. To identify the parameters in the model, key issues regarding excitation trajectory, filtering, and identification algorithm are discussed in detail. Finally, corresponding experiments are performed on the AFP machine, and experimental results show that there is a good agreement between the prediction of the model and the measurement in actuality. Data analysis shows that except for Z-axis, the relative error rates of the others are not greater than 5%, which proves the effectiveness of the established dynamic model and the identified parameters.

Identification and Estimation of Dynamic Heterogeneous Unbalanced Panel Data Models with Clustering

Identification and Estimation of Dynamic Heterogeneous Unbalanced Panel Data Models with Clustering

Optimization Based Excitation Signal Design Tailored to Application Specific Requirements

Data-driven identification of nonlinear dynamical black-box models is highly dependent on the excitation of the process the experimental data are gathered from. The optimized nonlinear input signal (OMNIPUS) is used to capture data in every operating region. The objective is uniform distribution of data points in the input space of the black-box model. For the industrial application of an OMNIPUS additional challenges occur which are tackled with novel extension of the design procedure of OMNIPUS. This includes constraints expressible by a classification function, speed-up of the execution time, and properties of data in case of unexpected termination of an experiment. Additionally, a model-based method is proposed to gain information about a suitable experiment duration for nonlinear system identification from the collected data. To validate these properties a combine harvester is chosen as industrial application. The data of this real-world process approve the stated advantageous properties.

DYNAMIC POTATO IDENTIFICATION AND CLEANING METHOD BASED ON RGB-D

To solve the problems of a large number of clods remaining in potatoes after mechanized harvesting in northern heavy clay soil planting areas in China and requiring much labor to separate clods from potatoes, which leads to a heavy workload, inefficiency and a low cleaning rate, an RGB-D-based Mask R-CNN dynamic potato identification model is established by using acquired RBG-D image data of untreated potatoes after mechanized harvesting, and a potato cleaning method is presented in this paper. This makes it possible to automatically separate clod impurities from potatoes. The experimental results showed that the prediction accuracy of the identification model is more than 97%. With the increase in cleaning conveyance speed, the prediction accuracy of the model and the actual cleaning precision show a downward trend. Comprehensively considering the potato cleaning efficiency and accuracy, when the speed is set to 0.4 m·s−1, the cleaning precision reaches as high as 96.35%. This research provides a method and theoretical reference for the further study of intelligent potato cleaning systems.

Dynamic identification and numerical model updating of an old bell-tower in Marche Region (Italy)

Dynamic identification and numerical model updating of an old bell-tower in Marche Region (Italy)

Research on Dynamic Modeling and Parameter Identification of 2R Coupling Drive Joint

Research on Dynamic Modeling and Parameter Identification of 2R Coupling Drive Joint

A Framework for Data Driven Dynamic Modeling of Serial Manipulators

Robot dynamic modeling and parameter identification are essential for many analyses. High-fidelity multi-body dynamics simulators can model the robot’s dynamic behavior, but they can’t identify the robot non-linear dynamic model, which is needed for controller design. This study proposes a three-step machine learning framework for obtaining the dynamic equations of serial manipulators from data. This framework consists of three steps. Initially, A library of candidate functions is first constructed, together with a data set based on robot unforced response. Secondly, the best models that can represent dynamic systems for each candidate function utilizing the training data set are then obtained using the SINDy-PI algorithm and Akaike Information Criterion (AIC). Through the MSE and test data, those best models will be reduced to get the functions that best describe the dynamic system. Finally, the dynamic equations that characterize the system are derived using the SINDy algorithm, including the applied force or torque. The technique was tested on three case studies —double pendulum, two-link of KUKA robot, and two-link of Stanford robot. The framework correctly determines the structure of the dynamic system and simultaneously accurately identified its parameters. The framework was able to deal with an ill-conditioned system of equations that arises for complex robot configuration.

Dynamic Model Identification for Whole-arm Compliance Control

Dynamic Model Identification for Whole-arm Compliance Control

Dynamic Model Identification for Insect Electroantennogram with Printed Electrode

Dynamic Model Identification for Insect Electroantennogram with Printed Electrode

A new dynamic stiffness model with hysteresis of air springs based on thermodynamics

A new theoretical model of the dynamic stiffness of air springs based on thermodynamics is proposed for determining the precise algorithm for air suspension control systems. The complex stiffness formula comprehensively considers the external heat exchange equivalent damping characteristics and air spring stiffness and damping characteristics with clear physical meaning and precise mathematical expressions for each contribution. Compared with the traditional air spring model, this model is universal because it does not restrict the gas change process inside air springs. Based on the new model, a set of identification methods for key nonlinear parameters are given. The indicator experiment was carried out to validate the dynamic stiffness model and parameter identification methods under various frequencies and strokes. Finally, the influence of key parameters and their dynamic characteristics are analysed. This theory and experiment identify air spring nonlinear parameter frequency dependence and change characteristics.

A Dynamic Model and Parameter Identification of High Viscosity Magnetorheological Fluid-Based Energy Absorber with Radial Flow Mode.

The excellent suspension stability of the high-viscosity linear polysiloxane magnetorheological fluid (HVLP MRF) makes it a great controlled medium for magnetorheological energy absorbers (MREAs). In our previous work, the Herschel–Bulkley flow model (HB model) was used to describe the shear-thinning rheological behavior and establish the dynamic model of an HVLP MRF-based MREA with radial flow mode. However, as the established model was implicit, the MREA response time increased and the buffer effect was degraded. To improve the time response characteristics, an explicit dynamic model based on the HB model incorporating minor losses (called the E-HBM model) is proposed in this study. The model parameters were identified based on the HBM model. To verify the E-HBM model, five evaluation parameters for the energy absorption performance of the MREA, that is, peak force, mean force, crush force efficiency, specific energy absorption, and stroke efficiency, were introduced to compare the theoretical results with the experimental results obtained using a high-speed drop tower facility with a mass of 600 kg. Then, the relative error of the crush force efficiency, specific energy absorption, and stroke efficiency was quantitatively and comprehensively analyzed considering the E-HBM model and experimental results. The results indicate that the proposed E-HBM model agrees with the impact behavior of the radial flow mode MREA.

A Graph Theory-Based Method for Dynamic Modeling and Parameter Identification of 6-DOF Industrial Robots

At present, the absolute positioning accuracy and control accuracy of industrial serial robots need to be improved to meet the accuracy requirements of precision manufacturing and precise control. An accurate dynamic model is an important theoretical basis for solving this problem, and precise dynamic parameters are the prerequisite for precise control. The research of dynamics and parameter identification can greatly promote the application of robots in the field of precision manufacturing and automation. In this paper, we study the dynamical modeling and dynamic parameter identification of an industrial robot system with six rotational DOF (6R robot system) and propose a new method for identifying dynamic parameters. Our aim is to provide an accurate mathematical description of the dynamics of the 6R robot and to accurately identify its dynamic parameters. First, we establish an unconstrained dynamic model for the 6R robot system and rewrite it to obtain the dynamic parameter identification model. Second, we establish the constraint equations of the 6R robot system. Finally, we establish the dynamic model of the constrained 6R robot system. Through the ADAMS simulation experiment, we verify the correctness and accuracy of the dynamic model. The experiments prove that the result of parameter identification has extremely high accuracy and the dynamic model can accurately describe the 6R robot system mathematically. The dynamic modeling method proposed in this paper can be used as the theoretical basis for the study of 6R robot system dynamics and the study of dynamics-based control theory.

Dynamic identification and radium–radon response mechanism of floor mixed water source in high ground temperature coal mine

With the deep mining of North China-type coalfield, the threat of mixed water source is increasingly serious, and water inrush accidents occur from time to time. Identifying the water inrush dynamic of limestone-mixed water source in high geothermal coal mine is difficult. This paper constructs a radioisotope distribution model of deep groundwater and obtains the dynamic data of radium and radon on the basis of the decay law of radionuclides and the principle of water balance and mass conservation. The radium–radon dating model is used to obtain the age of groundwater. Combined with the data of uranium and temperature, a dynamic identification model of multilayer limestone-mixed water source in coal seam floor is established by using a multielement mixed mass balance calculation method (M3). The relationship between the age of mixed water source and the mixing proportion of temperature and the contribution of recharge end member can be interpreted. The age of deep groundwater is negative correlation to the temperature. The temporal and spatial changes in mixed water sources and the response mechanism of radium–radon are described. Research results provide a new method to identify the source of water gushing in high geothermal coal mine, which is of great importance to improve coal mine safety production.

Identification of Dynamic Models by Using Metaheuristic Algorithms

A modified versions of metaheuristic algorithms are presented to compare their performance in identifying the structural dynamic systems. Genetic algorithm, biogeography based optimization algorithm, ant colony optimization algorithm and artificial bee colony algorithm are heuristic algorithms that have robustness and ease of implementation with simple structure. Different algorithms were selected some from evolution algorithms and other from swarm algorithms to boost the equilibrium of global searches and local searches, to compare the performance and investigate the applicability of proposed algorithms to system identification; three cases are suggested under different conditions concerning data availability, different noise rate and previous familiarity of parameters. Simulation results show these proposed algorithms produce excellent parameter estimation, even with little measurements and a high noise rate.

Identification of robot dynamic model and joint frictions using a baseplate force sensor

Identification of robot dynamic model and joint frictions using a baseplate force sensor

Identification and Prediction of Ship Maneuvering Motion Based on a Gaussian Process with Uncertainty Propagation

Maritime transport plays a vital role in economic development. To establish a vessel scheduling model, accurate ship maneuvering models should be used to optimize the strategy and maximize the economic benefits. The use of nonparametric modeling techniques to identify ship maneuvering systems has attracted considerable attention. The Gaussian process has high precision and strong generalization ability in fitting nonlinear functions and requires less training data, which is suitable for ship dynamic model identification. Compared with other machine learning methods, the most obvious advantage of the Gaussian process is that it can provide the uncertainty of prediction. However, most studies on ship modeling and prediction do not consider the uncertainty propagation in Gaussian processes. In this paper, a moment-matching-based approach is applied to address the problem. The proposed identification scheme for ship maneuvering systems is verified by container ship simulation data and experimental data from the Workshop on Verification and Validation of Ship Maneuvering Simulation Methods (SIMMAN) database. The results indicate that the identified model is accurate and shows good generalization performance. The uncertainty of ship motion prediction is well considered based on the uncertainty propagation technology.

Data-driven multi-model minimum variance controller design based on support vectors

Minimum variance (MV) benchmark is a quantification that is widely applied to compare actual performance of a control loop against its optimal performance. For linear systems, MV benchmark has been well studied, and several well-practiced techniques have been proposed to evaluate the performance of the systems. However, these techniques may not provide satisfactory performance in some situations. For instance, the operational data sampled from a multi-model system does not satisfy stationary conditions that are required for conventional methods. As a result, linear MV benchmark techniques cannot be applied. One possible approach to design MV controller for a multi-model system is to segregate, and label the data using a dynamic clustering method. Then, the MV benchmark can be determined by the multi-model MV control. In this paper, a support vector regression based method is proposed in which all three steps of dynamic clustering, model identification and MV controller design are performed simultaneously through a data driven approach. In addition, a new support vector-based clustering method is proposed by which the data are clustered in residual space of the process model. The optimality and convergence of the proposed algorithm are studied. The proposed multi-model MV controller design technique is demonstrated through a simulated continuous stirred-tank reactor (CSTR) example, which is operated in varying conditions.

Linear-extended-state-observer-based prescribed performance control for trajectory tracking of a robotic manipulator

Purpose Trajectory tracking error of robotic manipulator has limited its applications in trajectory tracking control systems. This paper aims to improve the trajectory tracking accuracy of robotic manipulator, so a linear-extended-state-observer (LESO)-based prescribed performance controller is proposed. Design/methodology/approach A prescribed performance function with the convergence rate, maximum overshoot and steady-state error is derived for the output error transformation, whose stability can guarantee trajectory tracking accuracy of the original robotic system. A LESO is designed to estimate and eliminate the total disturbance, which neither requires a detailed system model nor a heavy computation load. The stability of the system is proved via the Lyapunov theory. Findings Comparative experimental results show that the proposed controller can achieve better trajectory tracking accuracy than proportional-integral-differential control and linear active disturbance rejection control. Originality/value In the LESO-based prescribed performance control (PPC), the LESO was incorporated into the PPC design, it solved the problem of stabilizing the complex transformed system and avoided the costly offline identification of dynamic model and estimated and eliminated the total disturbance in real-time with light computational burden. LESO-based PPC further improved control accuracy on the basis of linear-active-disturbance-rejection-control. The new proposed method can reduce the trajectory tracking error of the robotic manipulators effectively on the basis of simplicity and stability.

Soft-constrained interval predictor models and epistemic reliability intervals: A new tool for uncertainty quantification with limited experimental data

Interval Predictor Models (IPMs) offer a non-probabilistic, interval-valued, characterization of the uncertainty affecting random data generating processes. IPMs are constructed directly from data, with no assumptions on the distributions of the uncertain factors driving the process, and are therefore exempt from the subjectivity induced by such a practice. The reliability of an IPM defines the probability of correct predictions for future samples and, in practice, its true value is always unknown due to finite samples sizes and limited understanding of the process. This paper proposes an overview of scenario optimization programs for the identification of IPMs. Traditional IPM identification methods are compared with a new scheme which softens the scenario constraints and exploits a trade-off between reliability and accuracy. The new methods allows prescribing predictors that achieve higher accuracy for a quantifiable reduction in the reliability. Scenario optimization theory is the mathematical tool used to prescribe formal epistemic bounds on the predictors reliability. A review of relevant theorems and bounds is proposed in this work. Scenario-based reliability bounds hold distribution-free, non asymptotically, and quantify the uncertainty affecting the model’s ability to correctly predict future data. The applicability of the new approach is tested on three examples: i) on the modelling of a trigonometric function affected by a noise term, ii) on the identification of a black-box system-controller dynamic response model and, iii) on the modelling of the vibration response of a car suspension arm crossed by a crack of unknown length. The points of strength and limitations of the new IPM are discussed based on the accuracy, computational cost, and width of the resulting epistemic bounds.